WO2022088729A1 - Procédé de positionnement de point et appareil associé, dispositif, support et programme informatique - Google Patents

Procédé de positionnement de point et appareil associé, dispositif, support et programme informatique Download PDF

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Publication number
WO2022088729A1
WO2022088729A1 PCT/CN2021/103150 CN2021103150W WO2022088729A1 WO 2022088729 A1 WO2022088729 A1 WO 2022088729A1 CN 2021103150 W CN2021103150 W CN 2021103150W WO 2022088729 A1 WO2022088729 A1 WO 2022088729A1
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target point
heat map
heat
target
positioning
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PCT/CN2021/103150
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English (en)
Chinese (zh)
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顾宇俊
袁璟
赵亮
黄宁
张少霆
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上海商汤智能科技有限公司
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Publication of WO2022088729A1 publication Critical patent/WO2022088729A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/70—Determining position or orientation of objects or cameras
    • G06T7/73—Determining position or orientation of objects or cameras using feature-based methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing
    • G06T2207/30008—Bone

Definitions

  • the present disclosure relates to the technical field of computer vision, and in particular, to a point positioning method and related apparatuses, devices, media and computer programs.
  • the embodiments of the present disclosure are expected to provide a point positioning method and related apparatus, equipment, medium and computer program.
  • a first aspect of the embodiments of the present disclosure provides a point positioning method, including: acquiring an image to be positioned; performing target point detection on the image to be positioned to obtain a coarse positioning heat map and a fine positioning heat map, wherein the coarse positioning heat map includes a target The heat value of the first area of the point is within the first heat value range; the heat value of the second area including the target point in the fine positioning heat map is within the second heat value range, where the first area is greater than the second area; The positioning heat map and the fine positioning heat map are used to obtain the position information of the target point.
  • the combined analysis of the coarse positioning heat map and the fine positioning heat map to obtain the position information of the target point includes: obtaining the position of the first target point in the coarse positioning heat map and the position of the first target point in the fine positioning heat map
  • the position information of the target point is obtained by processing the position of the first target point and the position of the second target point based on the confidence degree of the position of the second target point and the position of the second target point.
  • the confidence level of the position, the position information of the target point is obtained by processing the position of the first target point and the position of the second target point, including: determining the position of the first target point based on the heat value of the coarse positioning heat map; The heat value is used to determine the position of the second target point and the confidence of the position of the second target point; and based on the confidence of the position of the second target point, the position of the second target point or the position of the first target point is selected as the position information of the target point.
  • the second target point position or the first target point position is selected based on the confidence degree of the second target point position, and the position information as the target point includes: if the confidence degree of the second target point position If the position confidence condition is satisfied, the position of the second target point is used as the position information of the target point; if the confidence of the position of the second target point does not satisfy the position confidence condition, the position of the first target point is used as the position information of the target point.
  • the method before determining the position of the second target point and the confidence of the position of the second target point based on the heat value of the fine positioning heat map, the method further includes: determining the first target point based on the heat value of the coarse positioning heat map. A confidence level of the position of the target point. If the confidence level of the position of the first target point satisfies the confidence condition of the coarse position, the heat value in the heat map of the fine positioning which is located outside the preset distance range of the position of the first target point is adjusted to the preset heat value value; wherein, the preset heat value is outside the second heat value range.
  • the rough position confidence condition includes that the confidence level of the first target point position is greater than a first preset threshold
  • the fine position confidence condition includes at least one of the following: the confidence level of the second target point position is greater than the first target point position. Two preset thresholds, the confidence of the position of the second target point is greater than the confidence of the position of the first target point.
  • determining the position of the first target point based on the heat value of the coarse positioning heat map, or determining the position of the second target point based on the heat value of the fine positioning heat map includes: placing the positioning heat map The point with the largest heat value is used as the target point position, or the regional preset point in the positioning heat map is used as the target point position.
  • the confidence of the position of the first target point is determined based on the heat value of the coarse positioning heat map, or the confidence of the position of the second target point is determined based on the heat value of the fine positioning heat map, Including: obtaining at least one reference heat value; for each reference heat value, obtaining the size of the reference area whose heat value is greater than the reference heat value from the positioning heat map; and obtaining based on the size of each reference area and the heat value of the target point position The confidence of the target point position; or, based on the target point position of the positioning heat map, the target heat map is obtained; based on the similarity of the heat value distribution between the positioning heat map and the target heat map, the confidence of the target point position is obtained.
  • the acquiring at least one reference heat value includes: acquiring at least one magnification, and using the product between the at least one magnification and the heat value of the target point position as the at least one reference heat value; the reference area includes The location of the target point; the size of the reference area includes the perimeter and area of the reference area; the confidence of the target point location is obtained based on the size of each reference area and the heat value of the target point location, including: obtaining the area and circumference of each reference area The first ratio between the squares of the lengths is obtained by using the sum of the first ratios of the at least one reference area, the heat value of the target point position, and the preset heat value peak to obtain the confidence level of the target point position.
  • obtaining the target heat map based on the position of the target point in the positioning heat map includes: obtaining the heat value of each pixel in the target heat map by using a two-dimensional Gaussian function based on the position of the target point in the positioning heat map , where the exponent of the two-dimensional Gaussian function includes the range parameter, and the absolute value of the exponent is negatively correlated with the range parameter.
  • the range parameter in the dimensional Gaussian function based on the similarity of the heat value distribution between the positioning heat map and the target heat map, the confidence of the target point position is obtained, including: based on the heat value distribution between the positioning heat map and the target heat map, Obtain the correlation coefficient between the positioning heat map and the target heat map as the confidence of the target point location.
  • the method before performing target point detection on the image to be positioned to obtain the heat map of coarse positioning and the heat map of fine positioning, the method further includes: preprocessing the image to be positioned; wherein the preprocessing includes at least one of the following: The image to be positioned is normalized, and the image contrast of the image to be positioned is enhanced.
  • the method further includes: outputting the position information of the target point and the confidence level of the corresponding position information.
  • the normalizing the image to be positioned includes: setting a pixel value greater than a first pixel value in the image to be positioned as the first pixel value, and setting a pixel value of the image to be positioned smaller than a second pixel value as the first pixel value The pixel value of the pixel value is set as the second pixel value; wherein, among the sequentially arranged pixel values of the image to be positioned, the pixel value in the first numerical ranking is the first pixel value, and the pixel value in the second numerical ranking is the first pixel value. is the second pixel value.
  • performing target point detection on the image to be positioned to obtain a coarse positioning heatmap and a fine positioning heatmap includes: using a deep learning model to perform target point detection on the image to be positioned to obtain a coarse positioning heatmap and a fine positioning heatmap. Locate heatmaps.
  • the deep learning model is a fully convolutional neural network, and/or the deep learning model is trained by at least the following steps: acquiring a sample image, wherein the sample image is marked with the real image of the target point Location information; use the real location information of the target point to generate a coarse target heat map and a fine target heat map; wherein, the heat value of the third area containing the target point in the coarse target heat map is within the range of the third heat value; the fine target heat map The heat value of the fourth area including the target point is in the fourth heat value range, and the third area is larger than the fourth area; use the deep learning model to detect the target point of the sample image, and obtain the coarse positioning heat map and the fine positioning heat map ; Adjust the network parameters of the deep learning model based on the difference between the coarse target heatmap and the coarse localization heatmap and the difference between the fine target heatmap and the fine localization heatmap.
  • the to-be-located image is an X-ray image; in the first area and the second area, the closer to the target point the higher the heat value; the heat value outside the first area in the heat map is roughly located The value is lower than the lower limit value of the first heat value range, and the heat value outside the second area in the fine-location heat map is lower than the lower limit value of the second heat value range.
  • a second aspect of the embodiments of the present disclosure provides a point positioning device, including: an image acquisition module, a target detection module, and a position analysis module, an image acquisition module configured to acquire an image to be positioned; The target point is detected, and the coarse positioning heat map and the fine positioning heat map are obtained, wherein the heat value of the first area containing the target point in the coarse positioning heat map is within the first heat value range; the fine positioning heat map contains the target point.
  • the heat value of the area is in the second heat value range, wherein the first area is greater than the second area; the location analysis module is configured to combine and analyze the coarse positioning heat map and the fine positioning heat map to obtain the position information of the target point.
  • a third aspect of the embodiments of the present disclosure provides an electronic device, including a mutually coupled memory and a processor, where the processor is configured to execute program instructions stored in the memory, so as to implement the point positioning method in the first aspect.
  • a fourth aspect of the embodiments of the present disclosure provides a computer-readable storage medium on which program instructions are stored, and when the program instructions are executed by a processor, the point positioning method in the above-mentioned first aspect is implemented.
  • a fifth aspect of the embodiments of the present disclosure provides a computer program, where the computer program includes computer-readable codes, and when the computer-readable codes are executed in an electronic device, a processor of the electronic device executes commands for The point positioning method in the first aspect above is implemented.
  • a coarse positioning heat map and a fine positioning heat map are obtained, and the heat value of the first area including the target point in the coarse positioning heat map is in The first heat value range, the heat value of the second area including the target point in the fine positioning heat map is within the second heat value range, the first area of the coarse positioning heat map is larger than the second area of the fine positioning heat map, so the coarse positioning heat map
  • the map can represent the target response in a large range near the target point, and the fine positioning heat map can represent the target response in a small range near the target point. Therefore, combining the analysis of the coarse positioning heat map and the fine positioning heat map can have both coarse positioning heat map and fine positioning heat map at the same time.
  • the positioning stability of the positioning heat map and the accuracy of the fine positioning heat map can improve the accuracy and stability of point positioning.
  • FIG. 1 is a schematic flowchart of a point positioning method according to an embodiment of the present disclosure
  • FIG. 2 is an optional schematic diagram of a positioning result of an image to be positioned in an embodiment of the present disclosure
  • FIG. 3 is a schematic diagram of a coarse positioning heat map and a fine positioning heat map in an embodiment of the present disclosure
  • FIG. 4 is a schematic flowchart of training a deep learning model in an embodiment of the present disclosure
  • step S13 is a schematic flowchart of step S13 in the point positioning method according to an embodiment of the present disclosure
  • FIG. 6 is a schematic diagram of a frame of a point positioning device according to an embodiment of the present disclosure.
  • FIG. 7 is a schematic diagram of a framework of an electronic device according to an embodiment of the present disclosure.
  • FIG. 8 is a schematic diagram of a framework of a computer-readable storage medium according to an embodiment of the present disclosure.
  • system and “network” are often used interchangeably herein.
  • the term “and/or” in this article is only an association relationship to describe the associated objects, indicating that there can be three kinds of relationships, for example, A and/or B, it can mean that A exists alone, A and B exist at the same time, and A and B exist independently B these three cases.
  • the character "/” in this document generally indicates that the related objects are an “or” relationship.
  • “multiple” herein means two or more than two.
  • FIG. 1 is a schematic flowchart of a point positioning method according to an embodiment of the present disclosure. As shown in FIG. 1 , the point positioning method may include the following steps:
  • Step S11 Acquire an image to be positioned.
  • the image to be located may be an image including the facial features of a human face, so that target points such as the eyes, mouth, nose, etc. of the human face in the image can be located in the image by the point positioning method of this embodiment, so as to be subsequently used for the human face.
  • the image to be located may also be an image image including human tissue and organs, so that target points in human tissue and organs in the image image can be located by the point positioning method of this embodiment.
  • the image to be positioned may be an X-ray image, that is, a computed tomography image. In a specific implementation scenario, please refer to FIG.
  • the image to be located can be the X-ray image of the human lower limb (including the left lower limb and the right lower limb), and the target points obtained by positioning can include but are not limited to: the center of the femoral head, the tip of the greater trochanter, the medial malleolus of the femur, the lateral malleolus of the femur, and the medial endpoint of the tibial plateau , the outer end point of the tibial plateau, the inner end point of the ankle joint space, the outer end point of the ankle joint space, the target points shown in Figure 2 can be obtained through the point positioning method of this embodiment as the above-mentioned 8 target points on the left lower limb and the right lower limb, namely A total of 16 target points (black filled circles in Figure 2). In other application scenarios, it can be deduced by analogy, and no examples are given here.
  • the 16 target points in FIG. 2 are examples of target points to be positioned by using the point positioning method of this embodiment.
  • the above-mentioned 16 target points are not marked in the to-be-positioned image. It can be understood that the above 16 target points are removed from FIG. 2 .
  • the image behind the black-filled dots can be a to-be-located image.
  • Step S12 Perform target point detection on the image to be positioned to obtain a coarse positioning heat map and a fine positioning heat map.
  • the heat value of the first area including the target point in the coarse positioning heat map is within the first heat value range
  • the heat value of the second area including the target point in the fine positioning heat map is within the second heat value range
  • the first area is greater than the first area.
  • the positioning heat map can reflect the target response of each pixel in the image to be positioned. In some optional embodiments, in the first area and the second area, the heat value closer to the target point is higher, that is, the larger the target response value is.
  • the heat value outside the first area in the coarse positioning heat map is lower than the lower limit of the first heat value range
  • the heat value outside the second area in the fine positioning heat map is lower than the lower limit of the second heat value range
  • the coarse positioning heat map has a higher response value in a larger range than the fine positioning heat map, so the target points in the coarse positioning heat map and the fine positioning heat map can be easily determined, and the coarse positioning heat map can be located at subsequent points.
  • the stability of point positioning is ensured, and the fine positioning heat map can ensure the accuracy of point positioning in the subsequent point positioning process.
  • FIG. 3 is a schematic diagram of a coarse positioning heat map and a fine positioning heat map in an embodiment of the present disclosure.
  • (a) is a coarse positioning heat map
  • (b) is a fine positioning heat map.
  • Heat map for the convenience of description, the coarse positioning heat map and fine positioning heat map shown in Figure 3 represent the target response to the same target point, the solid circles in the coarse positioning heat map and the fine positioning heat map and their surrounding
  • the white filled areas respectively represent the first area and the second area containing the target points.
  • the heat value can also be represented in the order of spectral colors. For example, “red” can be used to represent the target with the largest heat value. Points, as the heat value decreases, the points gradually away from the target point are represented by "orange”, “yellow”, “green”, “blue”, etc. respectively.
  • a deep learning model in order to make full use of hardware parallel acceleration and reduce the complexity of target point detection, can be used to perform target point detection on the image to be positioned, thereby obtaining a coarse positioning heat map and a fine positioning heat map.
  • the deep learning model is a fully convolutional neural network.
  • a deep learning model can employ a Unet network with an encoder, decoder, and skip link structure. When using the deep learning model to detect the target point of the image to be positioned, the corresponding coarse positioning heat map and fine positioning heat map can be generated for each target point.
  • the to-be-located image may also be preprocessed before the target point detection is performed and the coarse positioning heat map and the fine positioning heat map are obtained;
  • the preprocessing may include normalizing the image to be positioned.
  • the normalizing the to-be-located image may include: setting a pixel value greater than a first pixel value in the to-be-located image as the first pixel value, and setting a pixel value of the to-be-located image smaller than a second pixel value as the first pixel value.
  • the pixel value of the value sets the second pixel value, wherein, among the sequentially arranged pixel values of the image to be positioned, the pixel value in the first numerical rank is the first pixel value, and the pixel value in the second numerical rank is the first pixel value.
  • sort the pixels of the image to be located according to the pixel value from small to large take the pixel value of the 99th percentile (that is, the 99th percentile of the total) as the first pixel value, and take the 3 percent (that is, the The pixel value ranked by the 3% bit) is the second pixel value, and the pixel value greater than the first pixel value is set as the first pixel value, and the pixel value smaller than the second pixel value is set as the second pixel value.
  • the first numerical ranking and the second numerical ranking may also be set according to specific applications, which are not limited herein.
  • the pixel value in the first numerical rank is the first pixel value
  • the pixel in the second numerical rank is the second pixel value
  • image contrast enhancement processing may be performed on the to-be-located image.
  • a Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm may be used to process the image to be positioned, so as to enhance the local contrast of the image.
  • Step S13 Combine and analyze the coarse positioning heat map and the fine positioning heat map to obtain the position information of the target point.
  • the position of the first target point and the confidence level of the position of the first target point in the rough positioning heat map may be obtained, and when the confidence level of the position of the first target point is greater than the confidence threshold
  • the position of the target point is used as the position information of the target point, so that the position information of the target point can be quickly determined under the condition that the accuracy requirement is not high.
  • the point with the highest heat value in the rough positioning heat map may be used as the position of the first target point.
  • the confidence level of the first target point position is used to indicate the reliability of the located first target point position, and the higher the confidence level of the first target point position, the higher the reliability of the first target point position.
  • the first confidence level is used to represent the confidence level of the position of the first target point.
  • the position of the first target point in the heat map of coarse positioning may also be obtained, and the position of the second target point and the confidence of the position of the second target point in the heat map of fine positioning may be obtained, and based on the position of the second target point in the heat map of fine positioning
  • the confidence level of the second target point position, the first target point position and the second target point position are processed to obtain the position information of the target point, and then the confidence level of the target point can be further based on the coarse positioning heat map and the fine positioning heat map. Therefore, the accuracy and stability of point positioning can be taken into account, and the accuracy of point positioning can be further improved.
  • the position of the first target point or the position of the second target point can be selected as the position information of the target point according to the confidence of the position of the second target point; or, according to the confidence of the position of the second target point, the output contains the first target point
  • the position information of the point position and the second target point position is not limited here.
  • the point with the highest heat value in the coarse positioning heat map may be used as the first target point position
  • the point with the highest heat value in the fine positioning heat map may be used as the second target point position.
  • the confidence level of the second target point position is used to indicate the reliability of the located second target point position, and the higher the confidence level of the second target point position, the higher the confidence level of the second target point position.
  • the second confidence level is used to represent the confidence level of the position of the second target point.
  • the position information of the target point position and the corresponding position may also be output confidence in the information.
  • the confidence level of the first target point can be used as the confidence level of the corresponding position information; or, when the position of the second target point is used as the position information of the target point, The confidence level of the second target point can be used as the confidence level of the corresponding position information, which can help the user to evaluate the position information of the target point obtained by the positioning and improve the user's perception.
  • a coarse positioning heat map and a fine positioning heat map are obtained, and the heat value of the first area including the target point in the coarse positioning heat map is within the first heat value range.
  • the heat value of the second area containing the target point in the fine positioning heat map is within the second heat value range
  • the first area of the coarse positioning heat map is larger than the second area of the fine positioning heat map, so the coarse positioning heat map can represent the target point.
  • the target response in a relatively large range nearby, and the fine positioning heat map can represent the target response in a small range near the target point, so combining the analysis of the coarse positioning heat map and the fine positioning heat map, the positioning of the coarse positioning heat map can be combined at the same time.
  • the stability and accuracy of the heat map for fine positioning can improve the accuracy and stability of point positioning.
  • FIG. 4 is a schematic flowchart of training a deep learning model in an embodiment of the present disclosure. As shown in FIG. 4 , the following steps may be included:
  • Step S41 Obtain a sample image, wherein the sample image is marked with the real position information of the target point.
  • the sample image may be an image including the facial features of a human face, and the target point may include at least one of the eyes, the mouth, and the nose of the human face.
  • the sample image may also be a video image including human tissues and organs.
  • the sample image may be an X-ray image, that is, a computed tomography image.
  • the sample image may be an X-ray image of a human lower limb (including a left lower limb and a right lower limb), and the target point may include, but is not limited to, at least one of the following: Bone center, tip of the greater trochanter, medial malleolus of femur, lateral malleolus of femur, medial end point of tibial plateau, lateral end point of tibial plateau, medial end point of ankle joint space, outer end point of ankle joint space, for details, please refer to the relevant steps in the previous embodiment, and will not be repeated here. Repeat.
  • Step S42 Generate a coarse target heat map and a fine target heat map by using the real position information of the target point.
  • the heat value of the third area including the target point in the coarse target heat map is within the third heat value range; the heat value of the fourth area including the target point in the fine target heat map is within the fourth heat value range, where the third area is greater than Fourth area.
  • the heat value of each pixel in the coarse target heat map and the fine target heat map can be obtained by using a two-dimensional Gaussian function based on the real position information of the target point in the sample image, wherein the index of the two-dimensional Gaussian function includes the range parameter. , and there is a negative correlation between the absolute value of the index and the range parameter, and the range parameter in the two-dimensional Gaussian function corresponding to the coarse target heatmap is larger than the range parameter in the two-dimensional Gaussian function corresponding to the fine target heatmap.
  • the heat value of each pixel in the target heat map can be expressed as:
  • (x, y) represents the abscissa and ordinate of the pixel point
  • (x 0 , y 0 ) represents the abscissa and ordinate of the target point
  • e is a natural constant
  • f(x, y) Represents the heat value of the pixel point
  • ⁇ represents the range parameter, which is used to control the size of the response area on the coarse target heat map and the fine target heat map
  • M represents the preset heat peak value, which is used to control the heat map peak value.
  • Step S43 Use the deep learning model to detect target points on the sample image, and obtain a heat map of coarse positioning and a heat map of fine positioning.
  • the deep learning model may be a fully convolutional neural network (Fully Convolutional Neural Networks), and a fully convolutional neural network may also be referred to as a fully convolutional network (Fully Convolutional Networks, FCN).
  • FCN Fully Convolutional Networks
  • Step S44 Adjust the network parameters of the deep learning model based on the difference between the coarse target heat map and the coarse positioning heat map and the difference between the fine target heat map and the fine positioning heat map.
  • the difference between the coarse target heat map and the coarse positioning heat map may include: a position difference between the points with the largest heat value in the heat map, a lower heat value in the heat map greater than the third heat range
  • the size difference between the regions of the limit For example, a mean square error function and a cross-entropy function can be used to process the above differences to obtain a first loss value corresponding to the difference between the coarse target heat map and the coarse positioning heat map.
  • the difference between the fine target heat map and the fine positioning heat map may include: the position difference between the points with the largest heat value in the heat map, the size between the areas in the heat map where the heat value is greater than the lower limit value of the fourth heat range difference.
  • a mean square error function and a cross-entropy function can be used to process the above differences to obtain a second loss value corresponding to the difference between the fine target heat map and the fine location heat map.
  • the difference between the coarse target heat map and the coarse positioning heat map, and the difference between the fine target heat map and the fine positioning heat map may also be weighted to obtain the total difference.
  • the above-mentioned first loss value and second loss value may be weighted to obtain the loss value of the deep learning model.
  • methods such as Stochastic Gradient Descent (SGD), Batch Gradient Descent (BGD), Mini-Batch Gradient Descent (MBGD), etc.
  • the network parameters of the learned model are adjusted.
  • batch gradient descent means that all samples are used to update parameters at each iteration; stochastic gradient descent means that one sample is used to update parameters at each iteration; mini-batch gradient descent means that at each iteration When , a batch of samples is used to update the parameters, which will not be repeated here.
  • the network parameters of the deep learning model may include: weights, biases and the like of hidden layer neurons.
  • a training end condition can also be set, and when the training end condition is satisfied, the training of the deep learning model can be ended.
  • the training end condition may include: the loss value of the deep learning model is smaller than the preset loss threshold, and the loss value is no longer reduced; and/or, the current training times reaches the preset times threshold (the preset times threshold is, for example, 500 times, 1000 times, etc.), which is not limited here.
  • the coarse target heat map and the fine target heat map are generated by using the real position information of the target point, and the heat value of the third area including the target point in the coarse target heat map is within the third heat value range, and the fine target heat map is The heat value of the fourth area including the target point in the target heat map is within the range of the fourth heat value, and the third area is larger than the fourth area. Therefore, the deep learning model is used to detect the target point of the sample image, and the heat map of coarse positioning and fine positioning are obtained. Heat map, based on the difference between the coarse target heat map and the coarse positioning heat map and the difference between the fine target heat map and the fine positioning heat map, adjusting the network parameters of the deep learning model can help the deep learning model to generate accurate heat maps. The coarse positioning heat map and the fine positioning heat map can help to improve the accuracy and stability of point positioning.
  • FIG. 5 is a schematic flowchart of step S13 in the point positioning method according to an embodiment of the present disclosure. As shown in Figure 5, the following steps may be included:
  • Step S131 Determine the position of the first target point based on the heat value of the rough positioning heat map.
  • the point with the largest heat value in the rough positioning heat map may be used as the position of the first target point.
  • a preset point in the first region (for example, the center of gravity of the first region) in the rough positioning heat map may also be used as the position of the first target point, which is not limited herein.
  • the difficulty of determining the position of the target point can be reduced and the speed of point positioning can be improved.
  • a first confidence level of the position of the first target point may also be determined.
  • at least one reference heat value may be obtained, for example, one reference heat value, two reference heat values, three reference heat values, etc. For each reference heat value, obtain the size of the reference area whose heat value is greater than the reference heat value from the rough positioning heat map, and obtain the first target point position based on the size of each reference area and the heat value of the first target point position. a confidence level.
  • the at least one reference heat value may be obtained by obtaining at least one magnification (eg, 0.2, 0.4, 0.6, 0.8, etc.), and comparing the at least one magnification (eg, 0.2, 0.4, 0.6, 0.8, etc.) with the first target, respectively
  • the product between the heat values of the point positions is used as at least one reference heat value.
  • the reference area includes the location of the target point; the size of the reference area includes the perimeter and area of the reference area, and the reference area is obtained based on the size of each reference area and the heat value of the location of the target point
  • the confidence of the target point position may include: obtaining a first ratio between the area of each reference area and the square of the perimeter, using the sum of the first ratios of at least one reference area, the heat value of the first target point position, and The preset heat peak value is obtained to obtain the first confidence level of the position of the first target point.
  • the first confidence level can be expressed as:
  • confidence represents the first confidence level
  • m represents the heat value of the position of the first target point
  • M represents the preset heat peak value
  • K represents the number of reference areas
  • s i represents the area of the ith reference area
  • a coarse target heat map may also be obtained based on the position of the first target point in the coarse positioning heat map, and based on the coarse positioning heat map The similarity of the heat value distribution between the image and the rough target heat map is obtained, and the first confidence level of the position of the first target point is obtained.
  • the heat value of each pixel in the coarse target heat map may be obtained by using a two-dimensional Gaussian function based on the position of the first target point in the coarse positioning heat map, wherein the exponent of the two-dimensional Gaussian function includes a range parameter, and the exponent There is a negative correlation between the absolute value of , and the range parameter; the range parameter in the two-dimensional Gaussian function corresponding to the coarse positioning heat map is greater than the range parameter in the two-dimensional Gaussian function corresponding to the fine positioning heat map .
  • the correlation coefficient between the coarse positioning heat map and the coarse target heat map may be obtained based on the heat value distribution between the coarse positioning heat map and the coarse target heat map, as the first target point position Confidence.
  • the confidence of the target point position or based on the similarity of the heat value distribution between the positioning heat map and the target heat map, the confidence of the target point position can be obtained, so it can improve the accuracy of the confidence, and at the same time, it can facilitate the subsequent biasing of the confidence.
  • the low area is repositioned and completed.
  • Step S132 Determine the position of the second target point and the confidence level of the position of the second target point based on the heat value of the fine positioning heat map.
  • the point with the largest heat value in the fine positioning heat map may be used as the second target point position.
  • a preset point in the second region (eg, the center of gravity of the second region) in the fine positioning heat map may also be used as the position of the second target point, which is not limited herein.
  • the difficulty of determining the position of the target point can be reduced and the speed of point positioning can be improved.
  • At least one reference heat value may be obtained, and for each reference heat value, from The size of the reference area whose heat value is greater than the reference heat value is obtained from the fine positioning heat map, so that the second confidence level of the position of the second target point is obtained based on the size of each reference area and the position of the second target point.
  • at least one reference heat value may be obtained by multiplying the at least one magnification and the heat value at the second target point respectively.
  • the reference area includes the location of the target point; the size of the reference area includes the perimeter and area of the reference area, and the reference area is obtained based on the size of each reference area and the heat value of the location of the target point
  • the confidence of the target point position may include: obtaining a first ratio between the area of each reference area and the square of the perimeter, using the sum of the first ratios of at least one reference area, the heat value of the second target point position, and Presetting the heat peak value to obtain the second confidence level of the position of the second target point, for details, please refer to the above-mentioned relevant steps, which will not be repeated here.
  • a fine target heat map may also be obtained based on the second target point position of the fine positioning heat map, and the fine target heat map may be obtained based on the fine positioning heat map and the fine target heat
  • the similarity of the heat value distribution between the graphs is obtained to obtain the confidence of the position of the second target point.
  • a two-dimensional Gaussian function may be used to obtain the heat value of each pixel in the fine-target heat map based on the position of the second target point in the fine-positioning heat map, wherein the exponent of the two-dimensional Gaussian function includes a range parameter, and the exponent of the exponent There is a negative correlation between the absolute value and the range parameter.
  • the range parameter in the two-dimensional Gaussian function corresponding to the coarse positioning heat map is larger than the range parameter in the two-dimensional Gaussian function corresponding to the fine positioning heat map.
  • the correlation coefficient between the fine positioning heat map and the fine target heat map may be obtained based on the similarity of the heat value distribution between the fine positioning heat map and the fine target heat map, as the second target point position
  • the second confidence level reference may be made to the foregoing related steps for details, and details are not repeated here.
  • the confidence of the target point position or based on the similarity of the heat value distribution between the positioning heat map and the target heat map, the confidence of the target point position can be obtained, so it can improve the accuracy of the confidence, and at the same time, it can facilitate the subsequent biasing of the confidence.
  • the low area is repositioned and completed.
  • the first target point in order to make the second target point position near the first target point position under the condition that the position of the first target point is relatively accurate, it is also possible to determine the first target point based on the heat value of the rough positioning heat map. a confidence level of the position of the target point, and when the confidence level of the position of the first target point satisfies the confidence condition of the coarse position, adjust the heat value in the heat map of the fine positioning outside the preset distance range of the position of the first target point to the preset heat value value, and the preset heat value is outside the second heat value range.
  • the preset heat value is set to 0, which is not limited here.
  • the position of the second target point and the confidence level of the position of the second target point may be determined.
  • the coarse position confidence condition may include that the confidence of the position of the first target point is greater than a first preset threshold (eg, 0.5, etc.).
  • the first target point position determined based on the coarse location heat map is basically accurate, so the second target point location and the second target point location can be obtained by combining the first target point location and the fine location heat map location.
  • the confidence level of the target point position is used for subsequent judgment, and the second target point position is located near the first target point position.
  • the position of the first target point and the first confidence level may also be directly output, which is not limited herein.
  • steps S131 and S132 may be performed in sequence, for example, step S131 is performed first and then step S132 is performed, or step S132 is performed first, and then step S131 is performed.
  • the foregoing step S131 and step S132 may also be performed simultaneously, which is not limited herein.
  • Step S133 Determine whether the confidence level of the position of the second target point satisfies the position confidence condition; if yes, execute step S134; otherwise, execute step S135.
  • the fine-tuned position confidence condition may include at least one of the following: the confidence level of the position of the second target point (ie, the second confidence level) is greater than a second preset threshold (eg, 0.5), the second target point The confidence of the position is greater than the confidence of the position of the first target point (ie, the first confidence), which can help to screen the position information of the target point with better confidence, which can help to improve the accuracy and stability of point positioning .
  • a second preset threshold eg, 0.5
  • step S134 can be performed, that is, the position of the second target point is taken as the position information of the target point; on the contrary, in order to ensure the stability of point positioning, step S135 can be performed, that is, the position of the first target point can be taken as the target point location information.
  • Step S134 Take the second target point position as the position information of the target point.
  • the position of the second target point may be used as the position information of the target point.
  • the confidence of the position of the second target point may be used as the confidence of the corresponding position information, and the position information of the target point and the corresponding position information can be output. Confidence of location information.
  • Step S135 Take the position of the first target point as the position information of the target point.
  • the position of the first target point can be used as the position information of the target point.
  • the confidence of the position of the first target point may be used as the confidence of the corresponding position information, and the position information of the position of the target point and The confidence level of the corresponding location information.
  • the point positioning process can be ended.
  • the first preset reliability threshold and the second preset reliability threshold may be set according to actual conditions, which are not limited herein.
  • the above objective reasons can also be output as a reminder, so as to avoid erroneous positioning with excessive deviation when it cannot be accurately positioned, and also facilitate subsequent completion.
  • the position of the first target point is determined by the coarse positioning heat map
  • the confidence of the position of the second target point and the position of the second target point is determined by fine positioning the heat value of the heat map, and when the second target point is obtained.
  • the position of the second target point is used as the position information of the target point
  • the position of the first target point is used as the position information of the target point.
  • the position information of the target point it can be beneficial to select the position information of the target point with better confidence, which can help to improve the accuracy and stability of the point positioning.
  • FIG. 6 is a schematic diagram of a frame of a point positioning device according to an embodiment of the present disclosure.
  • the positioning device 60 includes: an image acquisition module 61, a target detection module 62 and a position analysis module 63.
  • the image acquisition module 61 is configured to acquire an image to be positioned; the target detection module 62 is configured to perform target point detection on the image to be positioned.
  • the location analysis module 63 is configured to combine and analyze the coarse positioning heat map and the fine positioning heat map to obtain the position information of the target point.
  • a coarse positioning heat map and a fine positioning heat map are obtained, and the heat value of the first area including the target point in the coarse positioning heat map is within the first heat value range.
  • the heat value of the second area containing the target point in the fine positioning heat map is within the second heat value range
  • the first area of the coarse positioning heat map is larger than the second area of the fine positioning heat map, so the coarse positioning heat map can represent the target point.
  • the target response in a relatively large range nearby, and the fine positioning heat map can represent the target response in a small range near the target point, so combining the analysis of the coarse positioning heat map and the fine positioning heat map, the positioning of the coarse positioning heat map can be combined at the same time.
  • the stability and accuracy of the heat map for fine positioning can improve the accuracy and stability of point positioning.
  • the position analysis module 63 is configured to obtain the first target point position in the coarse positioning heat map and the confidence level of the second target point position and the second target point position in the fine positioning heat map, and based on the second target point position The confidence of the position of the target point, the position information of the target point is obtained by processing the position of the first target point and the position of the second target point.
  • the confidence of the second target point position is based on The position of the first target point and the position of the second target point are processed to obtain the position information of the target point, and then the confidence of the target point of the coarse positioning heat map and the fine positioning heat map can be further determined. position information, so as to further improve the accuracy of point positioning.
  • the location analysis module 63 includes a first analysis sub-module configured to determine the location of the first target point based on the heat value of the rough positioning heat map; the location analysis module 63 further includes a second analysis sub-module configured to be based on Finely locate the heat value of the heat map to determine the position of the second target point and the confidence of the position of the second target point; the position analysis module 63 also includes a position selection sub-module, configured to select the second target point based on the confidence of the position of the second target point. The target point position or the first target point position is used as the position information of the target point.
  • the position of the first target point is determined by the coarse positioning heat map
  • the confidence of the position of the second target point and the position of the second target point is determined by the heat map of fine positioning, and based on the position of the second target point. Confidence, select the position of the second target point or the position of the first target point as the position information of the target point.
  • the confidence of the position of the second target point can be determined based on the fine positioning heat map.
  • the position information of the selected target point in the first target point position determined by the heat map can be beneficial to improve the stability of point positioning.
  • the position selection sub-module includes a condition determination unit configured to determine whether the confidence of the position of the second target point satisfies the fine-tuned position confidence condition; the position selection sub-module further includes a position determination unit, configured to determine the condition determination unit in the When the confidence of the position of the second target point satisfies the position confidence condition, the position of the second target point is used as the position information of the target point, and the condition determination unit is further configured to determine that the confidence of the position of the second target point is not satisfied When the position information condition is finely determined, the position of the first target point is used as the position information of the target point.
  • the position of the second target point is used as the position information of the target point, and when the confidence of the position of the second target point does not meet the detailed position confidence condition.
  • the position of the first target point is used as the position information of the target point, so it can help to select the position information of the target point with better confidence, which can help to improve the accuracy and stability of point positioning.
  • the first analysis sub-module is further configured to determine the confidence level of the position of the first target point based on the heat value of the rough positioning heat map; the second analysis sub-module includes an adjustment unit, configured to be at the position of the first target point When the confidence level of the first target point satisfies the rough location confidence condition, the heat value in the fine positioning heat map that is outside the preset distance range of the first target point position is adjusted to the preset heat value; wherein, the preset heat value is within the second heat value range. outside.
  • the confidence of the position of the first target point satisfies the condition of coarse position confidence, directly adjust the heat value in the heat map of fine positioning outside the preset distance range of the position of the first target point to the preset heat value , to determine the confidence of the second target point position and the second target point position based on the heat value of the fine positioning heat map after the adjustment of the fine positioning heat map, so that the second target point position can be located at the first target point position nearby, further improving the accuracy of point localization.
  • the coarse position confidence condition includes that the confidence of the first target point position is greater than a first preset threshold
  • the fine position confidence condition includes at least one of the following: the confidence of the second target point position is greater than a second preset threshold , the confidence of the position of the second target point is greater than the confidence of the position of the first target point.
  • the coarse position confidence condition is set to include the confidence of the first target point position is greater than the first preset threshold
  • the fine position confidence condition is set to include the confidence of the second target point position is greater than the second preset. Threshold, the confidence of the position of the second target point is greater than at least one of the confidence of the position of the first target point, which can help to filter the position information of the target point with better confidence, which can help improve the accuracy of point positioning and stability.
  • the position analysis module 63 (specifically including the first analysis sub-module and the second analysis sub-module) is configured to: take the point with the largest heat value in the positioning heat map as the target point position, or use the position in the positioning heat map The area preset point is used as the target point position.
  • the difficulty of determining the target point position can be reduced, and the point positioning can be improved. speed.
  • the location analysis module 63 (specifically including the first analysis sub-module and the second analysis sub-module) is configured to obtain at least one reference heat value; for each reference heat value, obtain the heat value from the positioning heat map The size of the reference area larger than the reference heat value; the confidence of the target point position is obtained based on the size of each reference area and the heat value of the target point position; or, it is configured to obtain the target heat map based on the target point position of the positioning heat map; based on The similarity of the heat value distribution between the positioning heat map and the target heat map is obtained, and the confidence of the target point position is obtained.
  • the size of the reference area whose heat value is greater than the reference heat value is obtained from the positioning heat map, so as to be based on the size of each reference area and the target.
  • the heat value of the point position can obtain the confidence of the target point position, or based on the similarity of the heat value distribution between the positioning heat map and the target heat map, the confidence of the target point position can be obtained, so the accuracy of the confidence can be improved, and at the same time it can be It is convenient for subsequent relocation and completion of areas with low confidence.
  • the position analysis module 63 is configured to obtain at least one magnification, and use the product between the at least one magnification and the heat value of the target point position as at least one reference heat value, and the reference area includes the target point position;
  • the size includes the perimeter and the area of the reference region, is configured to obtain a first ratio between the area of each reference region and the square of the perimeter, using the sum of the first ratios of the at least one reference region, the heat value of the target point location, and Preset the heat peak value to get the confidence of the target point position.
  • the product between the at least one magnification and the heat value of the target point position is used as at least one reference heat value, so the reference heat value can be determined conveniently and quickly, which is beneficial to improve point positioning. and obtain the target by obtaining the first ratio between the area of each reference area and the square of the perimeter, using the sum of the first ratios of at least one reference area, the heat value of the target point location, and the preset heat value
  • the confidence of the position of the point can be accurately determined to obtain the confidence of the position of the target point.
  • the position analysis module 63 is configured to obtain the heat value of each pixel in the target heat map by using a two-dimensional Gaussian function based on the position of the target point in the positioning heat map, wherein the exponent of the two-dimensional Gaussian function includes a range parameter, And there is a negative correlation between the absolute value of the index and the range parameter, and the range parameter in the two-dimensional Gaussian function corresponding to the coarse positioning heat map is larger than the range parameter in the two-dimensional Gaussian function corresponding to the fine positioning heat map; it is also configured to be based on positioning The heat value distribution between the heat map and the target heat map, and the correlation coefficient between the positioning heat map and the target heat map is obtained as the confidence of the target point location.
  • the heat value of each pixel in the target heat map is obtained by using a two-dimensional Gaussian function, and the index of the two-dimensional Gaussian function includes a range parameter, and the absolute value of the index and range. There is a negative correlation between the parameters.
  • the range parameter in the two-dimensional Gaussian function corresponding to the coarse positioning heat map is larger than the range parameter in the two-dimensional Gaussian function corresponding to the fine positioning heat map, so the target heat map can be obtained conveniently and accurately, thereby Based on the heat value distribution between the positioning heat map and the target heat map, the correlation coefficient between the positioning heat map and the target heat map is obtained as the confidence of the target point position, so the confidence of the target point position can be easily and accurately obtained.
  • the point positioning device 60 further includes a preprocessing module, configured to pre-process the image to be positioned before the target detection module 62 performs target point detection on the image to be positioned to obtain a heat map of coarse positioning and a heat map of fine positioning. processing; wherein the preprocessing includes at least one of the following: normalizing the to-be-located image, and enhancing the image contrast of the to-be-located image.
  • the preprocessing includes normalizing the to-be-located image, and/or enhancing the image contrast of the to-be-located image, there can be a It is beneficial to improve the accuracy of subsequent target point detection.
  • the point positioning device 60 further includes an output module configured to, after the position analysis module 63 combines and analyzes the coarse positioning heat map and the fine positioning heat map to obtain the position information of the target point, The position information of the target point and the confidence level of the corresponding position information are output.
  • the preprocessing module includes a normalization sub-module configured to set a pixel value greater than the first pixel value in the image to be positioned as the first pixel value, and set a pixel value in the image to be positioned smaller than the second pixel value as the first pixel value The pixel value is set to the second pixel value; wherein, among the sequentially arranged pixel values of the image to be positioned, the pixel value in the first numerical ranking is the first pixel value, and the pixel value in the second numerical ranking is the second pixel value. Pixel values.
  • the pixel value in the first numerical ranking is the first pixel value
  • the pixel positioned in the second numerical ranking is the second pixel value
  • the target detection module 62 is configured to use a deep learning model to perform target point detection on the image to be positioned to obtain a coarse positioning heat map and a fine positioning heat map.
  • the hardware parallel acceleration can be fully utilized to reduce the complexity of target point detection.
  • the deep learning model is a fully convolutional neural network
  • the point positioning device 60 further includes a model training module, specifically including: a sample acquisition sub-module configured to acquire sample images, wherein the sample images are marked with The real position information of the target point; the heat map generation sub-module is configured to use the real position information of the target point to generate a coarse target heat map and a fine target heat map; wherein, the coarse target heat map includes the heat of the third area of the target point The value is in the third heat value range; the heat value of the fourth area including the target point in the fine target heat map is in the fourth heat value range, where the third area is larger than the fourth area; the target detection sub-module is configured to use deep learning
  • the model performs target point detection on the sample image, and obtains the coarse positioning heat map and the fine positioning heat map; the parameter adjustment sub-module is configured to be based on the difference between the coarse target heat map and the coarse positioning heat map and the fine target heat map and fine positioning heat map. The difference between the graph
  • the coarse target heat map and the fine target heat map are generated by using the real position information of the target point, and the heat value of the third area including the target point in the coarse target heat map is within the third heat value range, and the fine target heat map is The heat value of the fourth area including the target point in the target heat map is within the range of the fourth heat value, and the third area is larger than the fourth area. Therefore, the deep learning model is used to detect the target point of the sample image, and the heat map of coarse positioning and fine positioning are obtained. Heat map, based on the difference between the coarse target heat map and the coarse positioning heat map and the difference between the fine target heat map and the fine positioning heat map, adjusting the network parameters of the deep learning model can help the deep learning model to generate accurate heat maps. The coarse positioning heat map and the fine positioning heat map can help to improve the accuracy and stability of point positioning.
  • the image to be located is an X-ray image; in the first area and the second area, the heat value closer to the target point is higher; the heat value outside the first area in the rough positioning heat map is lower than the heat value in the first area and the second area.
  • a lower limit value of a range of heat values, and heat values located outside the second area in the fine positioning heat map are lower than the lower limit value of the second range of heat values.
  • FIG. 7 is a schematic frame diagram of an electronic device according to an embodiment of the present disclosure.
  • the electronic device 70 includes a memory 71 and a processor 72 coupled to each other, and the processor 72 is configured to execute program instructions stored in the memory 71 to implement the steps in any of the above-mentioned embodiments of the point positioning method.
  • the electronic device 70 may include, but is not limited to, a microcomputer and a server.
  • the electronic device 70 may also include mobile devices such as a notebook computer and a tablet computer, which are not limited herein.
  • the processor 72 is configured to control itself and the memory 71 to implement the steps in any of the above-mentioned embodiments of the point positioning method.
  • the processor 72 may also be referred to as a central processing unit (Central Processing Unit, CPU).
  • the processor 72 may be an integrated circuit chip with signal processing capability.
  • the processor 72 may also be a general-purpose processor, a digital signal processor (Digital Signal Processor, DSP), an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), a field programmable gate array (Field-Programmable Gate Array, FPGA) or other Programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.
  • a general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
  • the processor 72 may be co-implemented by an integrated circuit chip.
  • the above solution can have both the positioning stability of the coarse positioning heat map and the accuracy of the fine positioning heat map at the same time, thereby improving the accuracy and stability of point positioning.
  • FIG. 8 is a schematic diagram of a framework of a computer-readable storage medium according to an embodiment of the present disclosure.
  • the computer-readable storage medium 80 stores program instructions 801 that can be executed by the processor, and the program instructions 801 are used to implement the steps in any of the foregoing embodiments of the point positioning method.
  • It can have both the positioning stability of the coarse positioning heat map and the accuracy of the fine positioning heat map at the same time, so that the accuracy and stability of point positioning can be improved.
  • Embodiments of the present disclosure also provide a computer program, the computer program includes computer-readable codes, and when the computer-readable codes are executed in an electronic device, the processor of the electronic device executes the code to implement the above point positioning method.
  • the disclosed method and apparatus may be implemented in other manners.
  • the device implementations described above are only illustrative.
  • the division of modules or units is only a logical function division. In actual implementation, there may be other divisions.
  • units or components may be combined or integrated. to another system, or some features can be ignored, or not implemented.
  • the shown or discussed mutual coupling or direct coupling or communication connection may be through some interfaces, indirect coupling or communication connection of devices or units, which may be in electrical, mechanical or other forms.
  • Units described as separate components may or may not be physically separated, and components shown as units may or may not be physical units, that is, may be located in one place, or may be distributed over network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution in this implementation manner.
  • each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically alone, or two or more units may be integrated into one unit.
  • the above-mentioned integrated units may be implemented in the form of hardware, or may be implemented in the form of software functional units.
  • the integrated unit if implemented as a software functional unit and sold or used as a stand-alone product, may be stored in a computer-readable storage medium.
  • the technical solutions of the present application can be embodied in the form of software products in essence, or the parts that contribute to the prior art, or all or part of the technical solutions, and the computer software products are stored in a storage medium , including several instructions to make a computer device (which may be a personal computer, a server, or a network device, etc.) or a processor (processor) to execute all or part of the steps of the methods of the various embodiments of the present application.
  • the aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, ROM), random access memory (Random Access Memory, RAM), magnetic disk or optical disk and other media that can store program codes .

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Abstract

La présente invention concerne un procédé de positionnement de point et un appareil associé ainsi qu'un dispositif. Le procédé de positionnement de point comprend : l'acquisition d'une image à positionner (S11) ; la réalisation d'une détection de point cible sur ladite image, de façon à obtenir une carte thermique de positionnement approximatif et une carte thermique de positionnement précis (S12), une valeur thermique, qui est incluse dans la carte thermique de positionnement approximatif, d'une première zone d'un point cible se trouvant dans une première plage de valeurs thermiques, une valeur thermique, qui est incluse dans la carte thermique de positionnement précis, d'une seconde zone du point cible se trouvant dans une seconde plage de valeurs thermiques, et la première zone étant supérieure à la seconde zone ; et la combinaison et l'analyse de la carte thermique de positionnement approximatif et de la carte thermique de positionnement précis, de façon à obtenir des informations de position du point cible (S13).
PCT/CN2021/103150 2020-10-29 2021-06-29 Procédé de positionnement de point et appareil associé, dispositif, support et programme informatique WO2022088729A1 (fr)

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